Basic science of osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a prevalent, disabling disorder of the joints that affects a large population worldwide and for which there is no definitive cure. This review provides critical insights into the basic knowledge on OA that may lead to innovative end efficient new therapeutic regimens. While degradation of the articular cartilage is the hallmark of OA, with altered interactions between chondrocytes and compounds of the extracellular matrix, the subchondral bone has been also described as a key component of the disease, involving specific pathomechanisms controlling its initiation and progression. The identification of such events (and thus of possible targets for therapy) has been made possible by the availability of a number of animal models that aim at reproducing the human pathology, in particular large models of high tibial osteotomy (HTO). From a therapeutic point of view, mesenchymal stem cells (MSCs) represent a promising option for the treatment of OA and may be used concomitantly with functional substitutes integrating scaffolds and drugs/growth factors in tissue engineering setups. Altogether, these advances in the fundamental and experimental knowledge on OA may allow for the generation of improved, adapted therapeutic regimens to treat human OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".